CliMA / EnsembleKalmanProcesses.jl

What it does

• Derivative-free. no gradients of the forward model G are required — you only need to run it at different parameter values.
• Language-agnostic. G does not need to be written in Julia.
• Efficient at scale. computational complexity does not scale strongly with the number of parameters, and the methods tolerate noisy and complex parameter-to-data landscapes.
• Parallelizable. ensemble model evaluations are embarrassingly parallel, so you can exploit HPC resources (parallelism guide).
• Composable priors and observations. lego-like interfaces for building complex parameter distributions and observations.